The present invention relates to a system for combating pattern-dependent media noise in a signal. More particularly, the present invention relates to a system for reducing the total signal noise seen by a detector prior to detection using a pattern dependent equalization scheme.
Signals transmitted over a channel can be altered by noise or by the transmission medium, resulting in a distorted signal. The term “channel” is used herein to refer to a physical medium for transmitting data or for storing data. In data transmission, the channel can be a copper wire, an optical fiber, or air. In data storage, the channel can be a magnetic or optical medium.
Inter-symbol interference (ISI) refers to a signaling phenomenon where symbols blur into one another. Specifically, the transmission medium creates a “tail” of energy that lasts longer than intended, causing the transition edges between symbols in the signal to be less than precise. Thus, ISI describes the noise condition where energy from one symbol bleeds into adjacent symbols in a sequence. The received signal is then the sum of the distorted signals, making the effected symbol more susceptible to incorrect interpretation at the receiver.
Magnetic and optical recording channels are known to experience ISI. As the density of the recording media has increased, the rate of transitions within the recording signals has also increased, leading to more severe ISI because the frequency allows less time for the signal to settle between transitions. Conventionally, efforts to reduce or eliminate ISI using data independent equalization techniques sometimes cause noise enhancement due to the mismatch between the channel response and the equalization target. In other words, conventional channel equalization techniques tend to amplify interference at certain frequency ranges present at the receiver input.
Partial response maximum-likelihood (PRML) detection was advanced to reduce the noise enhancement resulting from equalization by allowing for a controlled amount of ISI. PRML detection schemes have been shown to achieve near optimal performance for additive white Gaussian noise (AWGN) channels when an appropriate Partial Response (PR) target is chosen. However, at higher recording densities (such as where the pulse width at the 50% amplitude point is equal to or greater than twice the period of the signal), the performance of the PRML detection scheme is severely degraded in media noise dominated channels.
In high area density recording systems, media noise may be responsible for more than 90% of the total noise power. Typically, media noise arises from fluctuations in the magnetization of the medium, and can be generally classified into three types of noise: transition noise, particulate noise and modulation noise. Particulate noise refers to signal interference due to random dispersion of magnetic particles or grains in the magnetic medium. Particulate noise is stationary, meaning that it is not dependent on user data recorded on the media. By contrast, transition noise and modulation noise are both non-stationary, meaning that they depend on the user data recorded in the media, or they are pattern-dependent.
Using first order approximation, it can be shown that the power-spectral density of transition noise is proportional to the linear recording density. Hence, transition noise becomes the main barrier to achieving ultra-high area densities on the recording media.
To account for the pattern-dependence of media noise, various advanced detectors have been proposed, which modify the branch metric calculation in Viterbi detectors to account for the correlation and data dependence of the noise. Another class of detectors utilizes decision-feedback equalization (DFE) to address pattern-dependent noise. An example of such a DFE is described by A. Kavcic in an article entitled “Decision Feedback Equalization in Channels with Signal-Dependent Media Noise”, published in IEEE Trans. on Magnetics, vol. 37, no. 4, July 2001, pp. 1909-1911.
All the above algorithms for combating pattern-dependent media noise can be characterized as “post-processing” architectures. In other words, these detectors presume a scenario where signals are corrupted by severe pattern-dependent correlated noise and compensate the performance loss by taking into account the pattern-dependence of the noise.